FDCluster: Mining frequent closed discriminative bicluster without candidate maintenance in multiple microarray datasets

Miao Wang, Xuequn Shang, Shaohua Zhang, Zhanhuai Li

科研成果: 书/报告/会议事项章节会议稿件同行评审

18 引用 (Scopus)

摘要

Biclustering is a methodology allowing for condition set and gene set points clustering simultaneously. Almost all the current biclustering algorithms find bicluster in one microarray dataset. In order to reduce the noise influence and find more biological biclusters, we propose an algorithm, FDCluster, to mine frequent closed discriminative bicluster in multiple microarray datasets. FDCluster uses Apriori property and several novel techniques for pruning to mine frequent closed bicluster without candidate maintenance. The experimental results show that FDCluster is more effectiveness than traditional method in either single micorarray dataset or multiple microarray datasets. We also test the biological significance using GO to show our proposed method is able to produce biologically relevant biclusters.

源语言英语
主期刊名Proceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
出版商Institute of Electrical and Electronics Engineers Inc.
779-786
页数8
ISBN(印刷版)9780769542577
DOI
出版状态已出版 - 2010

出版系列

姓名Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN(印刷版)1550-4786

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